Abstract
Background:
For estimation of fractions metabolized (fm) by different hepatic recombinant human CYP enzymes (rhCYP), calculation of inter-system extrapolation factors (ISEFs) has been proposed.
Methods:
ISEF values for CYP1A2, CYP2C19 and CYP3A4/5 were measured. A CYP2C9 ISEF was taken from a previous report. Using a set of compounds, fractions metabolized by CYP enzymes (fm,CYP) values calculated with the ISEFs based on rhCYP data were compared with those from the chemical inhibition data. Oral pharmacokinetics (PK) profiles of midazolam were simulated using the physiologically based pharmacokinetics (PBPK) model with the CYP3A ISEF. For other CYPs, the in vitro fm,CYP values were compared with the reference fm,CYP data back-calculated with, e.g. modeling of test substrates by feeding clinical PK data.
Results:
In vitro-in vitro fm,CYP3A4 relationship between the results from rhCYP incubation and chemical inhibition was drawn as an exponential correlation with R2=0.974. A midazolam PBPK model with the CYP3A4/5 ISEFs simulated the PK profiles within twofold error compared to the clinical observations. In a limited number of cases, the in vitro methods could not show good performance in predicting fm,CYP1A2, fm,CYP2C9 and fm,CYP2C19 values as reference data.
Conclusions:
The rhCYP data with the measured ISEFs provided reasonable calculation of fm,CYP3A4 values, showing slight over-estimation compared to chemical inhibition.
Acknowledgments
Special thanks go to Claire Juif, Judith Streckfuss and Marc Witschi for their support in the data generation.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Bjornsson TD, Callaghan JT, Einolf HJ, Fischer V, Gan L, Grimm S, et al. The conduct of in vitro and in vivo drug-drug interaction studies: a pharmaceutical research and manufacturers of America (PhRMA) perspective. Drug Metab Dispos 2003;31:815–32.10.1124/dmd.31.7.815Search in Google Scholar PubMed
2. Williams JA, Hurst SI, Bauman J, Jones BC, Hyland R, Gibbs JP, et al. Reaction phenotyping in drug discovery: moving forward with confidence? Curr Drug Metab 2003;4:527–34.10.2174/1389200033489235Search in Google Scholar PubMed
3. Proctor NJ, Tucker GT, Rostami-Hodjegan A. Predicting drug clearance from recombinantly expressed CYPs: intersystem extrapolation factors. Xenobiotica 2004;34:151–78.10.1080/00498250310001646353Search in Google Scholar PubMed
4. Crewe HK, Barter ZE, Yeo KR, Rostami-Hodjegan A. Are there differences in the catalytic activity per unit enzyme of recombinantly expressed and human liver microsomal cytochrome P450 2C9? A systematic investigation into inter-system extrapolation factors. Biopharm Drug Dispos 2011;32:303–18.10.1002/bdd.760Search in Google Scholar PubMed
5. Food and Drug Administration. Guidance for industry: drug interaction studies study design, data analysis, implications for dosing, and labeling recommendations. Rockville, Maryland, 2012.Search in Google Scholar
6. Huang SM, Strong JM, Zhang L, Reynolds KS, Nallani S, Temple R, et al. New era in drug interaction evaluation: US food and drug administration update on CYP enzymes, transporters, and the guidance process. J Clin Pharmacol 2008;48:662–70.10.1177/0091270007312153Search in Google Scholar PubMed
7. Khojasteh SC, Prabhu S, Kenny JR, Halladay JS, Lu AYH. Chemical inhibitors of cytochrome P450 isoforms in human liver microsomes: a re-evaluation of P450 isoform selectivity. Eur J Drug Metab Pharmacokinet 2011;36:1–16.10.1007/s13318-011-0024-2Search in Google Scholar PubMed
8. Lu C, Miwa GT, Prakash SR, Gan LS, Balani SK. A novel model for the prediction of drug-drug interactions in humans based on in vitro cytochrome P450 phenotypic data. Drug Metab Dispos 2007;35:79–85.10.1124/dmd.106.011346Search in Google Scholar PubMed
9. Njuguna NM, Umehara KI, Huth F, Schiller H, Chibale K, Camenisch G. Improvement of the chemical inhibition phenotyping assay by cross-reactivity correction. Drug Metab Pers Ther 2016;31:221–8.10.1515/dmpt-2016-0028Search in Google Scholar PubMed
10. Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition. J Pharm Sci 2002;91:1358–70.10.1002/jps.10128Search in Google Scholar PubMed
11. Peng Y, Wu H, Zhang X, Zhang F, Qi H, Zhong Y, et al. A comprehensive assay for nine major cytochrome P450 enzymes activities with 16 probe reactions on human liver microsomes by a single LC/MS/MS run to support reliable in vitro inhibitory drug–drug interaction evaluation. Xenobiotica 2015;8254:1–17.10.3109/00498254.2015.1036954Search in Google Scholar PubMed
12. Brown HS, Griffin M, Houston JB. Evaluation of cryopreserved human hepatocytes as an alternative in vitro system to microsomes for the prediction of metabolic clearance. Drug Metab Dispos 2007;35:293–301.10.1124/dmd.106.011569Search in Google Scholar PubMed
13. Rowland Yeo K, Rostami-Hodjegan A, Tucker GT. Abundance of cytochromes P450 in human liver: a meta-analysis. Br J Clin Pharmacol 2004;57:687–8.Search in Google Scholar
14. Chen Y, Liu L, Nguyen K, Fretland AJ. Utility of intersystem extrapolation factors in early reaction phenotyping and the quantitative extrapolation of human liver microsomal intrinsic clearance using recombinant cytochromes P450. Drug Metab Dispos 2011;39:373–82.10.1124/dmd.110.035147Search in Google Scholar PubMed
15. Lewis DF, Ito Y. Human CYPs involved in drug metabolism: structures, substrates and binding affinities. Expert Opin Drug Metab Toxicol 2010;6:661–74.10.1517/17425251003674380Search in Google Scholar PubMed
16. He M, Korzekwa KR, Jones JP, Rettie AE, Trager WF. Structural forms of phenprocoumon and warfarin that are metabolized at the active site of CYP2C9. Arch Biochem Biophys 1999;372:16–28.10.1006/abbi.1999.1468Search in Google Scholar PubMed
17. Stresser DM, Blanchard AP, Turner SD, Erve JC, Dandeneau AA, Miller VP, et al. Substrate-dependent modulation of CYP3A4 catalytic activity: analysis of 27 test compounds with four fluorometric substrates. Drug Metab Dispos 2000;28:1440–8.Search in Google Scholar PubMed
18. Kenworthy KE, Bloomer JC, Clarke SE, Houston JB. CYP3A4 drug interactions: correlation of 10 in vitro probe substrates. Br J Clin Pharmacol 1999;48:716–27.10.1046/j.1365-2125.1999.00073.xSearch in Google Scholar PubMed PubMed Central
19. Obach RS. The utility of in vitro cytochrome P450 inhibition data in the prediction of drug-drug interactions. J Pharmacol Exp Ther 2005;316:336–48.10.1124/jpet.105.093229Search in Google Scholar PubMed
20. Zhang H, Davis CD, Sinz MW, Rodrigues AD. Cytochrome P450 reaction-phenotyping: an industrial perspective. Expert Opin Drug Metab Toxicol 2007;3:667–87.10.1517/17425255.3.5.667Search in Google Scholar PubMed
Supplemental Material:
The online version of this article offers supplementary material (https://doi.org/10.1515/dmpt-2017-0024).
©2017 Walter de Gruyter GmbH, Berlin/Boston
